Considerations for Conducting Meta-analysis in Diagnostic Pathology
Notice bibliographique
Résumé
We read with great interest the recently published article entitled “Evidence-Based Pathology: Systematic Literature Reviews as the Basis for Guidelines and Best Practices” by Marchevsky and Wick1 and here submit the following comments concerning systematic review (SR) and meta-analysis (MA). Our comments seem supplemental to this elegant review of SR and MA.First, specific guidelines for conducting SR and MA have been developed and used, because the methodologic quality may vary significantly among studies, resulting in varied reliability, validity, and clinical applicability.2 These guidelines ensure a full disclosure of the process, reproducibility of the analysis process and results, and the inclusion of only qualified data/studies. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline is the most widely used.2 The recently published PRISMA-P was created for MA planning and protocol development.3 The Newcastle-Ottawa Scale, another system to evaluate study/data quality, may also be used in spite of being less popular than PRISMA.4 An international prospective registry of SR and MA has also been created and used, which may further reduce selection and other biases. Unfortunately, to our knowledge, the published SRs and MAs in diagnostic pathology seem to less frequently follow these guidelines (M.K., L.Z., unpublished data), which certainly deserves more awareness and utilization. All researchers (and our patients) would benefit from the consistent use of these tools in pathology.Second, data heterogeneity is a critical factor for data inclusion and analysis model selection. Therefore, before conducting an MA, heterogeneity of the included study results should be interrogated by using either the I2 test or the Cochran Q test; forest plots are a visual method to explore potential heterogeneity. If significant heterogeneity is present in the reported/included data, one may proceed with the following options: reporting a summary of reported/included data without further MA, subgroup and sensitivity analyses, and analyses using random-effects models (versus a fixed-effects model, which is appropriate for a homogeneous group of study results).Third, interested investigators may use commercial statistical software to conduct MAs, including SAS (SAS Institute Inc, Cary, North Carolina), Stata (Stata-Corp LP, College Station, Texas), and SPSS (IBM, Armonk, New York). Free software is also available with limited functionality. Review Manager or RevMan (The Cochrane Collaboration, The Nordic Cochrane Centre, Copenhagen, Denmark) is one of the leading choices. Open Meta-analysis (http://www.cebm.brown.edu/open_meta) was sponsored by the US Agency for Health Research and Quality and is very easy to use. MetaEasy (http://www.statanalysis.co.uk/meta-analysis.html), an add-on for Microsoft Excel (Microsoft, Redmond, Washington), may also be considered.In summary, we provide some additional information pertinent to the interesting article by Marchevsky and Wick.1 Our recent study shows that MA has been underutilized in diagnostic pathology despite its similar adjusted citation ratios to those of review articles.5 Our study also shows an increase in frequency of MA in diagnostic pathology in recent years, suggesting a growing interest in MA of pathology. Nonetheless, we agree that more efforts are needed to increase the awareness, training, and utilization of SR and MA in pathology.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,803 | 0,929 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,007 |
| Méta-épidémiologie (sens large) | 0,018 | 0,023 |
| Bibliométrie | 0,015 | 0,020 |
| Études des sciences et des technologies | 0,004 | 0,017 |
| Communication savante | 0,017 | 0,028 |
| Science ouverte | 0,015 | 0,010 |
| Intégrité de la recherche | 0,022 | 0,033 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».